This README showcases my expertise in machine learning, computer vision, and hardware design, highlighting projects that bridge AI research with practical applications through comprehensive educational resources and professional experience.

M.S. Electrical & Computer Engineering | Purdue University | Graduating December 2025

Areas of Interest: Signal & Image Processing, Deep Learning & Neural Networks, Computer Vision

Core Coursework:

• ECE 59500 - Introduction to Deep Learning (CNSIP & EE)
• ECE 62900 - Introduction to Neural Networks (CE & CNSIP)
• ECE 69500 - Deep Learning (CE & VLSI)
• ECE 60400 - Electromagnetic Field Theory (FO)
• ECE 60000 - Random Variable and Signals (CNSIP)
• ECE 595 - Boltzmann Law: Physics to ML (MN)
• ECE 595 - Computer Vision on Embedded Systems (CE)
• ENE 554 - Globalization & Engineering

Focus: Implementations of ML and CV algorithms for edge deployment and field optics

B.S. Computer Engineering | University of Michigan-Dearborn | Graduated with Distinction

Core Mentors: Dr. Paul Watta (ML/CV), Dr. Adnan Shaout (Hardware)

Foundation: VLSI Design, Embedded Systems, Autonomous Vehicle Perception (ECE270/370, ECE475)

Supervisor: Ozgur Erdinc - Senior Research Scientist

• Robotics Intelligence Lab: One of two researchers developing rapid prototype deployment and data collection methods training
• Utilized robot arms and structured light to enable smart detection of anonymous metal anomaly
• Recognition: Commendation letter from Pratt & Whitney and RTRC leadership

Principal Investigator: Dr. Paul Watta - Associate Professor

• Core team member for autonomous shuttle development (MDAS.ai)
• Utilized NVIDIA GTX TX2 for designing multi-input sensory feedback data collection pipeline over CAN bus

• Functional safety protocols (ISO 26262) and battery architecture development

WGAN-GP implementation with training dynamics analysis and mode collapse prevention research.

Supervisor: Dr. Adnan Shaout AI-powered feline identification using UNet segmentation and CNN classification with real-time embedded inference.

Full-stack parking management system integrating computer vision analysis, web backend, and iOS app.

Supervisor: Dr. Adnan Shaout Hardware MLP implementation in VHDL for FPGA deployment with transistor-level design.

Course: ECE370 with Dr. Paul Watta Python MLP implementation from scratch, providing first-principles understanding of neural network mathematics.

Interactive platform for knowledge topology analysis and meta-cognitive understanding of learning systems.

Advanced object detection systems with real-time inference and probabilistic confidence analysis.

• ECE270: Pattern Recognition Fundamentals
• ECE370: Neural Networks & Deep Learning

Advanced implementations of clustering, dimensionality reduction, and classification algorithms developed throughout graduate coursework.

Implemented Techniques: K-means clustering, hierarchical methods, PCA, t-SNE (MATLAB), manifold learning, pattern recognition systems.

Mathematical Foundations:

• Convolutional Neural Networks: Deep understanding of convolution operations, pooling, and feature extraction applied in ParkSmart parking analysis, CatNet feline identification, and MDAS.ai autonomous vehicle perception systems
• Random Variables & Stochastic Processes: Probabilistic modeling and uncertainty quantification for robust AI systems
• Signal Processing: Convolution, cross-correlation, frequency domain analysis, field theory
• Statistical Physics: Boltzmann distributions, thermodynamic principles
• Dimensionality Reduction: PCA, t-SNE, manifold learning techniques (MATLAB implementation)
• Optimization: Gradient descent, backpropagation, adversarial training dynamics

Software: PyTorch, TensorFlow, OpenCV, MATLAB, Python, C++, JavaScript/TypeScript

Hardware: VHDL, FPGA development, embedded systems, VLSI design, transistor-level implementation

Systems: Linux, Docker, AWS, React, D3.js, full-stack development

Introduced me to CV/ML through ECE270/370 coursework; supervised autonomous vehicle research (MDAS.ai project).

Supervised hardware neural network implementation (ECE475) and CatNet independent study.

Directed 2023 ML research internship focusing on mission-critical aerospace applications.

Current Focus: Computer vision for embedded systems, generative model stability, hardware-accelerated inference, interpretable AI systems

Future Directions: Edge AI optimization, advanced neural architectures, hardware-software co-design for intelligent systems

Philosophy: "Bridging cutting-edge ML research with practical implementations requires understanding systems from mathematical foundations through hardware constraints."

• Portfolio: rlr-github.github.io
• Professional: rodericklrenwick.com
• Research Platform: rory.software
• GitHub: github.com/RLR-GitHub

• Primary: RoderickLRenwick@gmail.com
• Academic: rrenwick@purdue.edu

Seeking: Research opportunities in ML/CV, hardware-accelerated AI, and edge deployment systems. Open to fellowships, collaborative research, and industry partnerships.

Unique Value: Hardware-software-theory integration enabling comprehensive AI system understanding from mathematical foundations to silicon implementation.